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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.3 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mistral-7b-instruct-v0.3-mimic4-adapt-multilabel-classify |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- F1 Micro: 0.0062 |
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- F1 Macro: 0.0059 |
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- Precision At 5: 0.0131 |
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- Recall At 5: 0.0040 |
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- Precision At 8: 0.0108 |
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- Recall At 8: 0.0056 |
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- Precision At 15: 0.0124 |
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- Recall At 15: 0.0101 |
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- Rare F1 Micro: 0.0040 |
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- Rare F1 Macro: 0.0040 |
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- Rare Precision: 0.0020 |
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- Rare Recall: 0.9992 |
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- Rare Precision At 5: 0.0055 |
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- Rare Recall At 5: 0.0025 |
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- Rare Precision At 8: 0.0041 |
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- Rare Recall At 8: 0.0029 |
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- Rare Precision At 15: 0.0032 |
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- Rare Recall At 15: 0.0044 |
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- Not Rare F1 Micro: 0.1354 |
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- Not Rare F1 Macro: 0.1308 |
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- Not Rare Precision: 0.0726 |
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- Not Rare Recall: 0.9998 |
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- Not Rare Precision At 5: 0.1391 |
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- Not Rare Recall At 5: 0.0842 |
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- Not Rare Precision At 8: 0.1066 |
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- Not Rare Recall At 8: 0.1005 |
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- Not Rare Precision At 15: 0.0989 |
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- Not Rare Recall At 15: 0.1650 |
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- Loss: -2.3104 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | F1 Micro | F1 Macro | Precision At 5 | Recall At 5 | Precision At 8 | Recall At 8 | Precision At 15 | Recall At 15 | Rare F1 Micro | Rare F1 Macro | Rare Precision | Rare Recall | Rare Precision At 5 | Rare Recall At 5 | Rare Precision At 8 | Rare Recall At 8 | Rare Precision At 15 | Rare Recall At 15 | Not Rare F1 Micro | Not Rare F1 Macro | Not Rare Precision | Not Rare Recall | Not Rare Precision At 5 | Not Rare Recall At 5 | Not Rare Precision At 8 | Not Rare Recall At 8 | Not Rare Precision At 15 | Not Rare Recall At 15 | Validation Loss | |
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|:-------------:|:------:|:----:|:--------:|:--------:|:--------------:|:-----------:|:--------------:|:-----------:|:---------------:|:------------:|:-------------:|:-------------:|:--------------:|:-----------:|:-------------------:|:----------------:|:-------------------:|:----------------:|:--------------------:|:-----------------:|:-----------------:|:-----------------:|:------------------:|:---------------:|:-----------------------:|:--------------------:|:-----------------------:|:--------------------:|:------------------------:|:---------------------:|:---------------:| |
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| -2.5733 | 0.9981 | 262 | 0.0086 | 0.0060 | 0.2032 | 0.0452 | 0.1975 | 0.0694 | 0.1826 | 0.1185 | 0.0051 | 0.0040 | 0.0026 | 0.7894 | 0.0369 | 0.0112 | 0.0329 | 0.0162 | 0.0290 | 0.0270 | 0.1354 | 0.1308 | 0.0726 | 1.0 | 0.2012 | 0.1187 | 0.1963 | 0.1842 | 0.1802 | 0.3115 | -2.1808 | |
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| -2.8745 | 1.9981 | 524 | 0.0070 | 0.0062 | 0.1153 | 0.0311 | 0.1079 | 0.0456 | 0.0933 | 0.0723 | 0.0044 | 0.0041 | 0.0022 | 0.8685 | 0.0391 | 0.0155 | 0.0333 | 0.0210 | 0.0281 | 0.0323 | 0.1399 | 0.1337 | 0.0754 | 0.9720 | 0.1735 | 0.1110 | 0.1544 | 0.1553 | 0.1400 | 0.2550 | -2.2971 | |
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| -3.0665 | 2.9981 | 786 | 0.0064 | 0.0060 | 0.0525 | 0.0148 | 0.0450 | 0.0203 | 0.0392 | 0.0309 | 0.0041 | 0.0040 | 0.0020 | 0.9688 | 0.0150 | 0.0061 | 0.0134 | 0.0086 | 0.0107 | 0.0129 | 0.1376 | 0.1323 | 0.0739 | 0.9840 | 0.1498 | 0.0950 | 0.1236 | 0.1245 | 0.1147 | 0.2041 | -2.3224 | |
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| -3.5627 | 3.9981 | 1048 | 0.0062 | 0.0060 | 0.0182 | 0.0059 | 0.0152 | 0.0075 | 0.0163 | 0.0135 | 0.0040 | 0.0040 | 0.0020 | 0.9920 | 0.0069 | 0.0031 | 0.0052 | 0.0039 | 0.0044 | 0.0062 | 0.1361 | 0.1313 | 0.0730 | 0.9973 | 0.1394 | 0.0855 | 0.1093 | 0.1055 | 0.1022 | 0.1756 | -2.3239 | |
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| -4.0526 | 4.9981 | 1310 | 0.0062 | 0.0059 | 0.0131 | 0.0040 | 0.0108 | 0.0056 | 0.0124 | 0.0101 | 0.0040 | 0.0040 | 0.0020 | 0.9992 | 0.0055 | 0.0025 | 0.0041 | 0.0029 | 0.0032 | 0.0044 | 0.1354 | 0.1308 | 0.0726 | 0.9998 | 0.1391 | 0.0842 | 0.1066 | 0.1005 | 0.0989 | 0.1650 | -2.3104 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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